Mass spectrometric imaging is a technique for investigating the distribution of a substance having a specific mass-to-charge ratio by performing a mass analysis on each of a plurality of micro areas within a two-dimensional area on a sample, such as a piece of biological tissue. This technique is expected to be applied, for example, in drug discoveries, biomarker discoveries, and investigation on the causes of various diseases. Mass spectrometers designed for mass spectrometric imaging are generally referred to as imaging mass spectrometers. This type of device may also be called a mass microscope since its operation normally includes the steps of performing a microscopic observation of an arbitrary area on a sample, selecting a region of interest based on the observed image, and performing an imaging mass analysis of the selected region. In the following description, the term “imaging mass spectrometer” will be used. Non-Patent Documents 1 and 2 disclose the configurations of commonly known imaging mass spectrometers and examples of analyses performed with those imaging mass spectrometers.
FIG. 9 schematically shows data obtained by performing an analysis with an imaging mass spectrometer and an image-displaying process based on the data. As shown, in an imaging mass spectrometer, an amount of mass analysis data is obtained for each of a large number of measurement points (micro areas) 102 within a two-dimensional area 101 on a sample 100. In the imaging mass spectrometers disclosed in Non-Patent Documents 1 and 2, ions originating from the sample are separated by a time-of-flight mass spectrometer according to their mass-to-charge ratio. In a system having such a configuration, a time-of-flight spectrum data showing a temporal change in the ion intensity can be obtained at each measurement point, and the time-of-flight values are converted into mass-to-charge ratios to create a mass spectrum. The spatial resolving power of the imaging mass spectrometer depends on the intervals of the measurement points 102 on the sample 100. To increase the spatial resolving power to obtain images with higher resolution, it is necessary to increase the number of measurement points 102. When a large number of measurement points 102 are set, an enormous amount of time-of-flight spectrum data will be obtained within the two-dimensional area 101 selected for the measurement.
Consider, for example, a case where a time-of-flight spectrum signal with a time range of approximately 20 msec is sampled at a sampling frequency of 1 GHz, and each sample of the signal is converted into a 16-bit digital signal. In this time-of-flight spectrum, the number of samples collected at each measurement point is approximately 20,000. Since each sample is a two-byte data, the total amount of data for one sample is approximately 40 kilobytes. If the measurement points are two-dimensionally arranged in a grid pattern of 250×250 pixels within the measurement area, the number of measurement points is 62,500, and the total amount of data obtained from the measurement area is as large as approximately 2.32 gigabytes. The total amount of data will further increase if the intervals of the measurement points are reduced to increase the number of measurement points so as to enhance the spatial resolving power, or if the two-dimensional area as the target of the measurement is enlarged. An additional increase in the total amount of data occurs when the sampling frequency for the time-of-flight spectrum signal is increased in order to improve the mass accuracy or mass-resolving power. Thus, mass spectrometric imaging data obtained with higher resolution and/or higher mass-resolving power will have larger data sizes.
To extract significant information from mass analysis data collected in the previously described manner, the spatial distribution of a mass-to-charge ratio corresponding to each peak on the mass spectrum must be visually presented to let an analysis operator interpret the significance of the data or perform an estimating process using a computer. To efficiently perform such tasks, as shown in FIG. 9, it is necessary to extract an intensity value corresponding to a specific mass-to-charge ratio (m/z=M1 in the example of FIG. 9) from the mass spectrum of each measurement point 102 and visually present the two-dimensional distribution of the intensity values at high speeds. For this purpose, a mass analysis data constituting a mass spectrum or time-of-flight spectrum must be loaded in the main memory (which is normally a random access memory) of a computer.
However, when a common type of personal computer is used, it is difficult to entirely load a high-resolution mass analysis data in the main memory since there is only a limited space practically available on the main memory. One technique for handling a large-size mass analysis data that cannot be entirely loaded in the main memory is to cut a portion of the data into a small size that can be loaded in the main memory and to create an image using that portion of the data. However, in this case, it is impossible to simultaneously display and analyze the data over a large spatial area and a wide mass range. One possible method for displaying and analyzing data over a large spatial area and a wide mass range is to use a portion of an external memory device (e.g. a hard disk) as a virtual main memory, which, however, inevitably causes a significant decrease in the processing speed. Furthermore, when the analysis is aimed at comparing data of two or more samples as well as processing the data of each sample, an even greater amount of data must be loaded in and processed on the main memory. It is practically impossible to process such a large amount of data with a commonly used personal computer.
A generally used technique for handling a large amount of data on a computer is to reduce the size of data by data compression. This technique can also be applied to the aforementioned large-size mass analysis data to reduce the data size so that the entire amount of data can be handled on the main memory of a computer. However, using a data compression technique to load the entire mass analysis data in the main memory in order to create a mass analysis result data or for other purposes causes the following problems.
As the method for compressing mass analysis data constituting a mass spectrum or time-of-flight spectrum, a method using interrelations among the neighboring data points is often adopted. For example, according to the technique described in Patent Document 1, a data compression based on the run-length encoding or entropy encoding may be performed for each of the mass spectrums shown in FIG. 9, using the interrelations of a plurality of data points neighboring each other on the mass-to-charge ratio axis. This operation reduces the size of the mass analysis data for each mass spectrum, i.e. for each measurement point 102, so that the mass analysis data of all the measurement points 102 within the two-dimensional area 101 on the sample 100 can be simultaneously loaded in the main memory.
When an analysis operator wants to observe a mass analysis result image for a certain mass-to-charge ratio, it is necessary, on the computer, to extract signal intensity information of the specified mass-to-charge ratio from the mass spectrum data of each measurement point and subject the extracted information to an image-creating process. If the mass analysis data stored in the main memory is an uncompressed data, the image can be easily created by reading intensity values from the memory addresses corresponding to the mass-to-charge ratio in question and reconstructing the read values into an image. On the other hand, if the mass analysis data is an encoded, compressed data, it is impossible to immediately tell the memory addresses corresponding to the specified mass-to-charge ratio. Accordingly, it is necessary to temporarily decompress the mass spectrum data before reading the intensity values corresponding to the specified mass-to-charge ratio.
The previously described process of collecting the intensity values corresponding to a specified mass-to-charge ratio must be repeated for each of the mass spectrums obtained at the measurement points, using a long period of time. Accordingly, a considerable amount of time is consumed to display one mass analysis result image. As already noted, the task of searching an enormous number of mass analysis data of various mass-to-charge ratios for a mass-to-charge ratio having a significant spatial distribution is essential for the imaging mass analysis. If the display of the mass analysis result image requires a long time, the throughput of the search will be significantly lowered.
As is commonly known, the technical field in which data compression techniques have the longest history and widest range of applications is image processing. This is because images inherently contain relatively large amounts of data and there was a great need for sending and receiving images having large amounts of data through communication channels with limited transmission rates or via other types of media with limited capacities. For example, the aforementioned run-length encoding is used in facsimiles, which handle black-and-white binary images. Such image-related fields have also been in great need for a method for quickly finding which position in the array of the compressed data corresponds to the desired position in the array of the original (uncompressed) data. For example, in a method for compressing a bitmap image data by run-length encoding proposed in Patent Document 2, an index showing the correspondence relationship between the position in the array of the original (uncompressed) data and the position in the array of the run-length-encoded data is used to improve the speed of finding the desired position. More specifically, for each of the positions defined at regular intervals in the array of compressed image data, index information indicating which position in the original data corresponds to that position is embedded.
Such a speed-up technique using the index information is also applicable to the compression of mass analysis data. However, in the method of embedding index information in the compressed data at regular intervals, the run-length encoding (or similar compressing process) must be completed for each section separated by the indices, which results in a decrease in the data-compression efficiency. Furthermore, the compressed data with index information embedded therein cannot be correctly read by a system designed to read compressed data without expecting the presence of index information in the data. That is to say, the embedding of index information prevents some of the existing systems from maintaining lower compatibility for the processing of the compressed mass analysis data.